CN105868543A - An inverse-Gaussian-life-distribution-based storage life test acceleration factor assessment method - Google Patents
An inverse-Gaussian-life-distribution-based storage life test acceleration factor assessment method Download PDFInfo
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Abstract
The invention provides an inverse-Gaussian-life-distribution-based storage life test acceleration factor assessment method comprising the steps of establishing a storage life model of electronic complete machine products based on competing failure and inverse-Gaussian life distribution; separately calculating the average storage life under practical using conditions and the average storage life under acceleration stress conditions of the electronic complete machine products based on inverse-Gaussian life distribution; calculating the acceleration factors of the electronic complete machine products according to the average storage life under the practical using conditions and the average storage life under the acceleration stress conditions. Thus, the inverse-Gaussian-life-distribution-based storage life test acceleration factor assessment method can accurately assess the storage life acceleration factors of electronic complete machine products.
Description
Technical field
The present invention relates to reliability test and assessment technology field, particularly relate to a kind of storage being distributed based on the inverse Gauss life-span
Deposit life test accelerated factor appraisal procedure.
Background technology
At present, storage life is the important war skill index that equipment contract (or charter) specifies.Carrying out complete machine
Storage-life accelerated test checking is with evaluation process, and due to machine product, it comprises multiple parts and material, and different parts
Rate of ageing different to the sensitivity of accelerated stress.When increasing stress with accelerated storage failure procedure, some of which is weak
The accelerated factor of link product is just relatively big than other weak link products, thus can produce and accelerate inconsistent problem.If appointing
Selecting the accelerated factor of a weak element as complete machine accelerated factor, its result is difficult to reflect practical situation.
Summary of the invention
In view of this, it is an object of the invention to propose a kind of storage life test being distributed based on the inverse Gauss life-span accelerate
Factor appraisal procedure, it is possible to realize the assessment exactly to electronic system product storage life accelerated factor.
The storage life test accelerated factor assessment being distributed based on the inverse Gauss life-span provided based on the above-mentioned purpose present invention
Method, including step:
Set up the electronic system product storage life model of distribution of inverse Gauss life-span based on competing failure;
According to life model, calculate electronic system product that the inverse Gauss life-span be distributed putting down under active usage conditions respectively
The average storage life-span under the conditions of equal storage life and accelerated stress;
According to the average storage life-span under the conditions of the average storage life-span under described actual service conditions and accelerated stress, meter
Calculate the accelerated factor obtaining electronic system product.
In certain embodiments, the described electronic system product storage being distributed based on the inverse Gauss life-span setting up competing failure
Life model, including:
Based on competing failure model, set up the Reliability Model of electronic system product;
According to the Reliability Model of electronic system product, use the inverse Gauss life-span to be distributed and carry out storage life modeling.
In certain embodiments, described based on competing failure model, set up the step of the Reliability Model of electronic system product
Suddenly include:
Competitive fault model is defined as: if machine product has n kind Failure Factors, and each Failure Factors is the most independent
Act on described machine product, and the most corresponding certain out-of-service time, any of which Failure Factors all can cause complete machine
Product failure, in all of Failure Factors, when that Failure Factors produced the earliest occurs, will cause machine product to lose efficacy,
The i.e. machine product out-of-service time is:
T=min{T1,T2,...,Tn,
Wherein, T is the machine product out-of-service time, TiFor the out-of-service time of any Failure Factors, n is appointing more than or equal to 1
Meaning natural number;
Assume FiT () is the accumulative failure distribution function of the out-of-service time of any Failure Factors, then machine product is accumulative
Failure distribution function is:
Wherein, FiT () is similar and different distribution, but above formula requires that this n distribution must be independent, when them it
Between the most immediately, in the case of i.e. a kind of Failure Factors can cause another kind of Failure Factors, then must take into each Failure Factors it
Between influence each other, need above formula is modified:
When arbitrary Failure Factors works, the reliability of its correspondence is:
Wherein, λiT () is the crash rate of corresponding i-th Failure Factors, when n factor works simultaneously, machine product
Reliability Model will is that
In certain embodiments, the described Reliability Model according to machine product, use the inverse Gauss life-span to be distributed and store
The step depositing modeling for life includes:
For electronic or electromechanical complicated machine product, it is generally recognized that its building block, the life-span of device are distributed as inverse Gauss
Distribution:
In formula: μ is referred to as location parameter;ν becomes form parameter.
Therefore, the life-span distribution making dead wind area be described electronic or electromechanical complicated machine product, if arbitrary composition portion
Part, the parameter of device are ui,vi, arbitrary building block, the probability density function of device be:
The life-span distribution making dead wind area be described electronic or electromechanical complicated machine product, if the ginseng of arbitrary Failure Factors
Number is ui,vi, the probability density function of arbitrary Failure Factors is:
Overall to the life-span obeying dead wind area, its mean time between failures is: Ti=ui, therefore, described electronics
Or the storage life modeling formula of the complicated machine product of electromechanics is:
In certain embodiments, the electronic system product that described calculating was distributed based on the inverse Gauss life-span is at actual service conditions
Under average storage life-span and accelerated stress under the conditions of the average storage life-span, including:
The electronic system product average storage life-span under active usage conditions is:
If machine product a certain parts weak link i under a certain environment accelerated stress conditioning (i=1,2 ...,
N) corresponding accelerated factor is Ai, electronic system product average life under accelerated stress level is:
Wherein, μAComplete machine is average life under the conditions of accelerated stress;AequipmentThe actual accelerated factor of complete machine;μ0—
Average life under the conditions of complete machine normal stress;μiCorresponding to the average life of weak link i under the conditions of using;N complete machine is thin
Weak link product number.
In certain embodiments, under the conditions of according to described average storage life-span under active usage conditions and accelerated stress
The average storage life-span, calculate equipment complete machine actual accelerated factor be:
According to the average life under described accelerated stress, show that the accelerated factor of inverse Gauss model electronic system product is:
From the above it can be seen that electronic system product be distributed based on the inverse Gauss life-span acceleration that provides of the present invention because of
Sub-appraisal procedure, machine product storage life model on the basis of, according to product under natural storage state with acceleration mode
Under the equal principle of storage reliability, being directed to the life-span obeys the electronic system product of dead wind area and gives storage life
The appraisal procedure of test accelerated factor.
Accompanying drawing explanation
The stream of the storage life test accelerated factor appraisal procedure that Fig. 1 is the embodiment of the present invention to be distributed based on the inverse Gauss life-span
Journey schematic diagram;
Fig. 2 is the electronic system product storage longevity being distributed based on the inverse Gauss life-span that the embodiment of the present invention sets up competing failure
The schematic flow sheet of life model.
Detailed description of the invention
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and reference
Accompanying drawing, the present invention is described in more detail.
It should be noted that the statement of all uses " first " and " second " is for distinguishing two in the embodiment of the present invention
The entity of individual same names non-equal or the parameter of non-equal, it is seen that " first " " second ", only for the convenience of statement, should not
Being interpreted as the restriction to the embodiment of the present invention, this is illustrated by subsequent embodiment the most one by one.
As embodiments of the invention, refering to shown in Fig. 1, the storage being distributed based on the inverse Gauss life-span for the embodiment of the present invention
The schematic flow sheet of life test accelerated factor appraisal procedure.The described storage life test being distributed based on the inverse Gauss life-span adds
The method of speed factor assessment includes:
Step 101, sets up the electronic system product storage life model being distributed based on the inverse Gauss life-span of competing failure.Tool
Body implementation process is as follows, as shown in Figure 2:
Step 201: based on competing failure model, set up the Reliability Model of electronic system product;
Competing failure is the important failure mode of one of product.In reliability theory, product loses the function of defined
It is referred to as losing efficacy.For large product, due to its internal structure and the complexity of external working environment thereof, cause the thing of product failure
Reason, chemical principle are multiple because often having, if occurring any reason all to cause product failure, this product is called Tests With Competing Causes of Failure under Exponential Distribution
(Competing Failure Modes).The reason causing product failure is referred to as the failure mechanism (Failure of product
Mechanism).Such as, in the life test of cable, the reason of cable failure is caused to have: cable is breakdown, leakage current
Index exceedes regulation critical point and artificial disconnection etc., and any of which reason is referred to as the failure mechanism of product.
Concrete, in some optional embodiments, above-mentioned steps can further include steps of
Competitive fault model is defined as: if machine product has n kind Failure Factors, and each Failure Factors is the most independent
Act on described machine product, and the most corresponding certain out-of-service time, any of which Failure Factors all can cause complete machine
Product failure, in all of Failure Factors, when that Failure Factors produced the earliest occurs, will cause machine product to lose efficacy,
The i.e. machine product out-of-service time is:
T=min{T1,T2,...,Tn(1),
Wherein, T is the machine product out-of-service time, TiFor the out-of-service time of any Failure Factors, n is appointing more than or equal to 1
Meaning natural number;
Assume FiT () is the accumulative failure distribution function of the out-of-service time of any Failure Factors, then machine product is accumulative
Failure distribution function is:
Wherein, FiT () can be similar and different distribution, but above formula (2) requires that this n distribution must be independent,
Between them the most immediately, in the case of i.e. a kind of Failure Factors can cause another kind of Failure Factors, then must take into each mistake
Influencing each other between effect factor, accordingly, it would be desirable to above formula (2) is modified:
When arbitrary Failure Factors works, the reliability of its correspondence is:
Wherein, λiT () is the crash rate of corresponding i-th Failure Factors, when n factor works simultaneously, machine product
Reliability Model will is that
Total crash rate of machine product will be n the independent crash rate sum of corresponding moment t, it may be assumed that
λ (t)=λ1(t)+λ2(t)+...+λn(t)(5)
Formula (5) is referred to as the addition criterion of Tests With Competing Causes of Failure under Exponential Distribution crash rate.
Step 202: according to the Reliability Model of machine product, uses the inverse Gauss life-span to be distributed and carries out storage life modeling.
For electronic or electromechanical complex device, generally it can be thought that the life-span of its building block, device is distributed as inverse Gauss
Distribution:
In formula: μ is referred to as location parameter;ν becomes form parameter.
Therefore, the life-span distribution making dead wind area be described electronic or electromechanical complicated machine product, if arbitrary composition portion
Part, the parameter of device are ui,vi, arbitrary building block, the probability density function of device be:
Overall to the life-span obeying dead wind area, its mean time between failures is: Ti=ui, therefore, described electronics
Or the storage life modeling formula of the complicated machine product of electromechanics is:
Step 102, calculates the electronic system product being distributed based on the inverse Gauss life-span under active usage conditions flat respectively
The average storage life-span under the conditions of equal storage life and accelerated stress.
As an embodiment, the electronic system product that described calculating was distributed based on the inverse Gauss life-span is at actual service conditions
Under average storage life-span and accelerated stress under the conditions of the average storage life-span, including: a certain parts of electronic system product are in reality
Under the conditions of border uses, average life is:
If complete machine a certain parts weak link i under a certain action of environmental stresses (i=1,2 ..., n) corresponding acceleration
The factor is Ai.Complete machine average life under accelerated stress level is:
Wherein, μAComplete machine is average life under the conditions of accelerated stress;AequipmentThe actual accelerated factor of complete machine;μ0—
Average life under the conditions of complete machine normal stress;μiCorresponding to the average life of weak link i under the conditions of using;N complete machine is thin
Weak link product number.
Step 103, according to the average storage under the conditions of the average storage life-span under described actual service conditions and accelerated stress
Deposit the life-span, be calculated the accelerated factor of electronic system product.
In an embodiment, according to putting down under the conditions of described average storage life-span under active usage conditions and accelerated stress
All storage life, can show that the accelerated factor of inverse Gauss model equipment is:
From above-described embodiment it can be seen that equipment accelerated factor be distributed based on the inverse Gauss life-span that the present invention provides is assessed
Method, can make full use of the accelerated test information of primer, components and parts and parts, and result confidence level is high;And consider
The accelerated factor of all parts weights influence to complete machine accelerated factor, the most reasonable;Finally, whole described based on
The equipment accelerated factor appraisal procedure that the inverse Gauss life-span is distributed is compact, it is easy to accomplish.
Those of ordinary skill in the field are it is understood that the discussion of any of the above embodiment is exemplary only, not
It is intended to imply that the scope of the present disclosure (including claim) is limited to these examples;Under the thinking of the present invention, above example
Or can also be combined between the technical characteristic in different embodiments, step can realize with random order, and exists such as
Other change of the many of the different aspect of the upper described present invention, in order to concisely they do not provide in details.
It addition, for simplifying explanation and discussing, and in order to obscure the invention, can in the accompanying drawing provided
To illustrate or can not illustrate and integrated circuit (IC) chip and the known power supply/grounding connection of other parts.Furthermore, it is possible to
Device is shown in block diagram form, in order to avoid obscuring the invention, and this have also contemplated that following facts, i.e. about this
The details of the embodiment of a little block diagram arrangements be the platform that depends highly on and will implement the present invention (that is, these details should
In the range of being completely in the understanding of those skilled in the art).Elaborating that detail (such as, circuit) is to describe the present invention's
In the case of exemplary embodiment, it will be apparent to those skilled in the art that can there is no these details
In the case of or these details change in the case of implement the present invention.Therefore, these descriptions are considered as explanation
Property rather than restrictive.
Although invention has been described to have been incorporated with the specific embodiment of the present invention, but according to retouching above
Stating, a lot of replacements, amendment and the modification of these embodiments will be apparent from for those of ordinary skills.Example
As, other memory architecture (such as, dynamic ram (DRAM)) can use discussed embodiment.
Embodiments of the invention be intended to fall into all such replacement within the broad range of claims,
Amendment and modification.Therefore, all within the spirit and principles in the present invention, any omission of being made, amendment, equivalent, improvement
Deng, should be included within the scope of the present invention.
Claims (6)
1. the storage life test accelerated factor appraisal procedure being distributed based on the inverse Gauss life-span, it is characterised in that include step
Rapid:
Set up the electronic system product storage life model of distribution of inverse Gauss life-span based on competing failure;
According to life model, the electronic system product that the calculating inverse Gauss life-span is distributed respectively average storage under active usage conditions
Deposit the average storage life-span under the conditions of life-span and accelerated stress;
According to the average storage life-span under the conditions of the average storage life-span under described actual service conditions and accelerated stress, calculate
Accelerated factor to electronic system product.
Method the most according to claim 1, it is characterised in that described set up being distributed based on the inverse Gauss life-span of competing failure
Electronic system product storage life model, including:
Based on competing failure model, set up the Reliability Model of electronic system product;
According to the Reliability Model of electronic system product, use the inverse Gauss life-span to be distributed and carry out storage life modeling.
Method the most according to claim 2, it is characterised in that described based on competing failure model, sets up complete electronic set and produces
The step of the Reliability Model of product includes:
Competitive fault model is defined as: if machine product has a n kind Failure Factors, and the work that each Failure Factors is the most independent
For described machine product, and the most corresponding certain out-of-service time, any of which Failure Factors all can cause machine product
Lost efficacy, in all of Failure Factors, when that Failure Factors produced the earliest occurs, machine product will be caused to lose efficacy, the most whole
The machine product failure time is:
T=min{T1,T2,...,Tn,
Wherein, T is the machine product out-of-service time, TiFor the out-of-service time of any Failure Factors, n is any natural more than or equal to 1
Number;
Assume FiT () is the accumulative failure distribution function of the out-of-service time of any Failure Factors, then the accumulative inefficacy of machine product divides
Cloth function is:
Wherein, FiT () is similar and different distribution, but above formula requires that this n distribution must be independent, between them not
Time independent, in the case of i.e. a kind of Failure Factors can cause another kind of Failure Factors, then must take between each Failure Factors
Influence each other, need above formula is modified:
When arbitrary Failure Factors works, the reliability of its correspondence is:
Wherein, λiT () is the crash rate of corresponding i-th Failure Factors, when n factor works simultaneously, and machine product reliable
Degree model will is that
Method the most according to claim 3, it is characterised in that the described Reliability Model according to machine product, uses inverse
The Gauss life-span is distributed and carries out the step of storage life modeling and include:
For electronic or electromechanical complicated machine product equipment, it is generally recognized that its building block, the life-span of device are distributed as inverse Gauss
Distribution:
In formula: μ is referred to as location parameter;ν becomes form parameter.
Therefore, the life-span distribution making dead wind area be described electronic or electromechanical complicated machine product, if arbitrary building block, device
The parameter of part is ui,vi, arbitrary building block, the probability density function of device be:
The life-span distribution making dead wind area be described electronic or electromechanical complicated machine product, if the parameter of arbitrary Failure Factors is
ui,vi, the probability density function of arbitrary Failure Factors is:
Overall to the life-span obeying dead wind area, its mean time between failures is: Ti=ui, therefore, described electronics or machine
The storage life modeling formula replying miscellaneous machine product by cable is:
Method the most according to claim 1, it is characterised in that the complete electronic set that described calculating was distributed based on the inverse Gauss life-span
The average storage life-span under the conditions of product average storage life-span under active usage conditions and accelerated stress, including:
The electronic system product average storage life-span under active usage conditions is:
If machine product a certain parts weak link i under a certain environment accelerated stress conditioning (i=1,2 ..., n) right
The accelerated factor answered is Ai, electronic system product average life under accelerated stress level is:
Wherein, μAComplete machine is average life under the conditions of accelerated stress;AequipmentThe actual accelerated factor of complete machine;μ0Complete machine
Average life under the conditions of normal stress;μiCorresponding to the average life of weak link i under the conditions of using;N complete machine weakness ring
Joint product number.
Method the most according to claim 5, it is characterised in that according to the described average storage longevity under active usage conditions
In the average storage life-span under the conditions of life and accelerated stress, the actual accelerated factor calculating equipment complete machine is:
According to the average life under described accelerated stress, show that the accelerated factor of inverse Gauss model electronic system product is:
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CN108333208A (en) * | 2018-01-22 | 2018-07-27 | 航天科工防御技术研究试验中心 | A kind of complete machine grade product storage-life accelerated test method |
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CN109827662A (en) * | 2019-01-22 | 2019-05-31 | 江苏双汇电力发展股份有限公司 | Determination method based on dead wind area low resistance insulator infrared detection temperature threshold |
CN110263487A (en) * | 2019-07-02 | 2019-09-20 | 中国航空综合技术研究所 | A kind of engineering goods accelerate fatigue life test method |
CN110414086A (en) * | 2019-07-10 | 2019-11-05 | 北京华安中泰检测技术有限公司 | A kind of combined stress accelerated factor calculation method based on sensitivity |
CN111898236A (en) * | 2020-05-25 | 2020-11-06 | 中国航天标准化研究所 | Acceleration factor analysis method for accelerated storage test of electronic complete machine based on failure big data |
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CN106407555A (en) * | 2016-09-14 | 2017-02-15 | 中国人民解放军海军航空工程学院 | Accelerated degradation data analysis method based on principle of invariance of accelerating factor |
CN108333208A (en) * | 2018-01-22 | 2018-07-27 | 航天科工防御技术研究试验中心 | A kind of complete machine grade product storage-life accelerated test method |
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CN108399278A (en) * | 2018-01-24 | 2018-08-14 | 航天科工防御技术研究试验中心 | A kind of multifactor accelerated factor computational methods of electronics |
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CN109827662B (en) * | 2019-01-22 | 2020-08-04 | 江苏双汇电力发展股份有限公司 | Method for judging infrared detection temperature threshold value of low-value insulator based on inverse Gaussian distribution |
CN110263487A (en) * | 2019-07-02 | 2019-09-20 | 中国航空综合技术研究所 | A kind of engineering goods accelerate fatigue life test method |
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CN110414086B (en) * | 2019-07-10 | 2023-01-17 | 北京华安中泰检测技术有限公司 | Sensitivity-based comprehensive stress acceleration factor calculation method |
CN111898236A (en) * | 2020-05-25 | 2020-11-06 | 中国航天标准化研究所 | Acceleration factor analysis method for accelerated storage test of electronic complete machine based on failure big data |
CN111898236B (en) * | 2020-05-25 | 2024-01-09 | 中国航天标准化研究所 | Acceleration factor analysis method for accelerated storage test based on failure big data |
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